{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actor Critic Policy Optimization with TensorFlow\n",
    "\n",
    "In this notebook, we will look at policy optimization using TensorFlow. Let us quickly go through the derivation of what is policy Optimization and then we will apply this to CartPole environment - just like what we did in Chapter 6. \n",
    "\n",
    "\n",
    "### Derivation of Policy Gradient\n",
    "In Policy Optimization, we will have a neural network which takes in state `s` as input and produces the `logits` for action probabilities. \n",
    "\n",
    "The policy is parameterized by $\\theta$\n",
    "$$\\pi_\\theta(a|s)$$\n",
    "\n",
    "The agent follows the policy and generates the trajectory $\\large \\tau$ \n",
    "\n",
    "$$ s_1 \\rightarrow a_1 \\rightarrow s_2 \\rightarrow a_2 \\rightarrow .... \\rightarrow s_{T-1} \\rightarrow a_{T-1} \\rightarrow s_T \\rightarrow a_T$$ \n",
    "\n",
    "here $s_T$ is not necessarily the terminal state but some time horizon T upto which we are looking at the trajectory. \n",
    "\n",
    "The probability of trajectory $\\large \\tau$ depends on the transition probabilities $p(s_t+1 | s_t, a_t)$ and the policy $\\pi_\\theta(a_t|s_t)$. It is given by the expression:\n",
    "\n",
    "$$p_\\theta(\\tau) = p_\\theta(s_1, a_1, s_2, a_2, ..., s_T, a_T) = p(s_1)\\prod_{t=1}^{T}\\pi_\\theta(a_t|s_t)p(s_{t+1}|s_t,a_t)$$\n",
    "\n",
    "The expected return from following the policy $\\pi$ is given by:\n",
    "\n",
    "$$J(\\theta) = {\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ \\sum_{t} \\gamma^t r(s_t, a_t) \\right]$$\n",
    "\n",
    "We want to find the $\\theta$ which maximizes the expected reward/return $J(\\theta)$. In other words, the optimal $\\theta=\\theta^*$ is given by expression\n",
    "\n",
    "$$\\theta^* = arg \\underset{\\theta}{max}{\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ \\sum_{t} \\gamma^t r(s_t, a_t) \\right] $$\n",
    "\n",
    "\n",
    "Moving on, let us try to find the optimal $\\theta$. To keep the notations easier to understand, we will replace $\\sum_{t} \\gamma^t r(s_t, a_t)$ as $r(\\tau)$:\n",
    "\n",
    "$$J(\\theta) = {\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ r(\\tau) \\right] = \\int p_\\theta(\\tau)r(\\tau) d\\tau$$\n",
    "\n",
    "We take the gradient/derivative of above expression with respect to $\\theta$:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\nabla_{\\theta} \\int p_\\theta(\\tau)r(\\tau) d\\tau $$\n",
    "\n",
    "By linearity we can move the gradient inside the integral:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\int \\nabla_{\\theta} p_\\theta(\\tau)r(\\tau) d\\tau $$\n",
    "\n",
    "Using log derivative trick, we know that $\\nabla_x f(x) = f(x) \\nabla_x \\log{f(x)}$. Using this we can write above expression as:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\int p_\\theta(\\tau) \\left[ \\nabla_{\\theta}\\log{p_\\theta(\\tau)} r(\\tau) \\right] d\\tau $$\n",
    "\n",
    "We can now write the integral back as expectation, which gives us the expression:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  {\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ \\nabla_{\\theta}\\log{p_\\theta(\\tau)} r(\\tau) \\right] $$\n",
    "\n",
    "Let us now expand the term $\\nabla_{\\theta}\\log{p_\\theta(\\tau)}$ by writing out the full expression of $p_\\theta(\\tau)$. \n",
    "\n",
    "$$ \\nabla_{\\theta}\\log{p_\\theta(\\tau)}  = \\nabla_{\\theta} \\log{ \\left[ p(s_1) \\prod_{t=1}^{T}\\pi_\\theta(a_t|s_t)p(s_{t+1}|s_t,a_t)\\right]}$$\n",
    "\n",
    "We know that log of product of terms can be written as sum of log of terms i.e. \n",
    "\n",
    "$$\\log{\\prod_i f_i(x)} = \\sum_i log{f_i(x)}$$ \n",
    "\n",
    "Using the above substitution, we get:\n",
    "\n",
    "$$ \\nabla_{\\theta}\\log{p_\\theta(\\tau)}  = \\nabla_{\\theta} \\left[ log{p(s_1)} +  \\sum_{t=1}^{T} \\left\\{ \\log{ \\pi_\\theta(a_t|s_t)} + \\log{p(s_{t+1}|s_t,a_t)} \\right\\} \\right]$$\n",
    "\n",
    "The only term dependent on $\\theta$ is $\\pi_\\theta(a_t|s_t)$. The other two terms $log{p(s_1)}$ and $\\log{p(s_{t+1}|s_t,a_t)}$ do not depend on $\\theta$. Accordingly, we can simplify the above expression as:\n",
    "\n",
    "$$ \\nabla_{\\theta}\\log{p_\\theta(\\tau)}  = \\sum_{t=1}^{T} \\nabla_{\\theta} \\log{ \\pi_\\theta(a_t|s_t)} $$\n",
    "\n",
    "\n",
    "Substituting the above term into the expression for $\\nabla_{\\theta} J(\\theta)$, as well as expanding $r(\\tau)$ we get:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  {\\large E}_{\\tau \\sim p_\\theta(\\tau)} \\left[ \\left( \\sum_{t=1}^{T} \\nabla_{\\theta} \\log{ \\pi_\\theta(a_t|s_t)} \\right) \\left( \\sum_{t=1}^{T} \\gamma^t r(s_t, a_t) \\right) \\right] $$\n",
    "\n",
    "We can now replace the outer expectation with an estimate over multiple trajectories to get the following expression for the gradient of policy objective:\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\frac{1}{N} \\sum_{i=1}^{N} \\left[ \\left( \\sum_{t=1}^{T} \\nabla_{\\theta} \\log{ \\pi_\\theta(a_t^i|s_t^i)} \\right) \\left( \\sum_{t=1}^{T} \\gamma^t r(s_t^i, a_t^i) \\right) \\right] $$\n",
    "\n",
    "where i denotes the $i^{th}$ trajectory. \n",
    "\n",
    "To improve the policy, we now take a +ve step in $\\theta$ in the direction of $\\nabla_{\\theta} J(\\theta)$:\n",
    "\n",
    "$$\\theta = \\theta + \\alpha \\nabla_{\\theta} J(\\theta)$$\n",
    "\n",
    "To summarize, we design a model which takes state $s$ as input and produces the policy distribution $\\pi_\\theta(a|s)$ as the output of the model. We use a policy to generate returns and then change the model parameter $\\theta$ using the expression: $\\theta = \\theta + \\alpha \\nabla_{\\theta} J(\\theta)$\n",
    "\n",
    "\n",
    "### Rewards to Go Trick\n",
    "\n",
    "\n",
    "we drop the reward terms that came before time t as at time t, the action we take can only impact the reward which comes at time t and later. This leads to changing the 2nd inner sum going from t’=t to T instead of earlier sum over t’ going from t’=1 to T. i.e. the start index is now t’=t and not t=1. The revised expression is given below:\n",
    "\n",
    "\n",
    "$$\\nabla_{\\theta} J(\\theta) =  \\frac{1}{N} \\sum_{i=1}^{N} \\left[  \\sum_{t=1}^{T}  \\left( \\nabla_{\\theta} \\log{ \\pi_\\theta(a_t^i|s_t^i)} \\sum_{t'=t}^{T} \\gamma^{t'-t} r(s_{t'}^i, a_{t'}^i) \\right) \\right] $$\n",
    "\n",
    "\n",
    "### Baseline Trick\n",
    "We can further reduce the variance using base line to get:\n",
    "\n",
    "$$\\nabla_\\theta J\\left(\\theta\\right)=\\frac{1}{N}\\sum_{i=1}^{N}\\sum_{t=1}^{T}{\\nabla_\\theta\\log{\\pi_\\theta\\left(a_t^i\\middle| s_t^i\\right)\\ }\\left[\\hat{Q}(s_t^i,\\ a_t^i) - b\\left(s_t^i\\right)\\right]\\\\}$$\n",
    "\n",
    "where:\n",
    "\n",
    "$$\\hat{Q}(s_t^i,\\ a_t^i)=\\ \\sum_{t^\\prime=t}^{T} \\gamma^{t'-t} r\\left(s_{t^\\prime}^i,a_{t^\\prime}^i\\right)$$\n",
    "\n",
    "\n",
    "### Actor Critic\n",
    "In Actor Critic, we fit the baseline to an estimator for state value V. We use a model as given below:\n",
    "\n",
    "\n",
    "![Actor Critic](./images/actor_critic.png \"Actor Critic\")\n",
    "\n",
    "\n",
    "\n",
    "The final update rule under Actor Critic is given below:\n",
    "\n",
    "$$\\nabla_{\\theta,\\phi} J\\left(\\theta,\\phi \\right)=\\frac{1}{N}\\sum_{i=1}^{N}\\sum_{t=1}^{T}{\\nabla_\\theta\\log{\\pi_\\theta\\left(a_t^i\\middle| s_t^i\\right)\\ }\\left[ \\hat{Q}(s_t^i,\\ a_t^i) -\\ V_\\phi(s_t^i)\\right]\\ }$$\n",
    "\n",
    "\n",
    "### Implementing Loss and Gradient Step in PyTorch/TensorFlow\n",
    "\n",
    "We will implement a pseudo loss function, whose derivative will give us $\\nabla_{\\theta,\\phi} J\\left(\\theta,\\phi \\right)$. Also as PyTorch/TensorFlow carryout a gradient Step, we will convert maximization to minimization by changing the sign of this objective function\n",
    "\n",
    "$$L_{CrossEntropy}(\\theta, \\phi) = - J(\\theta, \\phi) = - \\frac{1}{N} \\sum_{i=1}^{N}  \\sum_{t=1}^{T} \\left( \\log{\\pi_\\theta (a_t^i | s_t^i) } \\left[ \\hat{Q}(s_t^i,\\ a_t^i) - V_\\phi(s_t^i)\\right] \\right)$$\n",
    "\n",
    "To summarize, we will pass the state `s` through the network to get $\\log{ \\pi_\\theta(a_t^i|s_t^i)}$ and $V_\\phi(s_t)$. We will calculate the cross_entropy loss for the actions actually seen in the trajectory. We will then calculate the weighted mean of these individual loss terms in the trajectory with weights being the Advantage $\\hat{A}(s_t^i,\\ a_t^i) = \\hat{Q}(s_t^i,\\ a_t^i) - V_\\phi(s_t^i)$\n",
    "\n",
    "This will be followed by a gradient step in -ve direction of weighted NLL (negative log loss) i.e. in positive direction of the gradient of $J(\\theta, \\phi)= - L_{CrossEntropy}(\\theta, \\phi)$ \n",
    "\n",
    "We also add a regularization term known as Entropy. Entropy of a distribution is defined as:\n",
    "\n",
    "$$H(X) = \\sum_x -p(x).log(p(x))$$\n",
    "\n",
    "To keep enough exploration, we will want the probability to have a spread out distribution and not let the probability distribution to collapse to a single value or a small region too soon. Bigger the spread of a distribution, higher the entropy H(x) of a distribution. Accordingly, the term fed into PyTorch/TensorFlow minimizer is:\n",
    "\n",
    "\n",
    "$$Loss(\\theta, \\phi) = - J(\\theta, \\phi) - H(\\pi_\\theta(a_t^i|s_t^i)) = - \\frac{1}{N} \\sum_{i=1}^{N} \\left[ \\sum_{t=1}^{T} \\left(  \\log{ \\pi_\\theta(a_t^i|s_t^i)} \\left[ \\hat{Q}(s_t^i,\\ a_t^i) - V_\\phi(s_t^i)\\right] \\right) - \\beta \\sum_a \\pi_\\theta(a|s_t^i).\\log{ \\pi_\\theta(a|s_t^i)} \\right] $$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers \n",
    "from tensorflow.keras import Model, Input\n",
    "import gym\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import convolve, gaussian\n",
    "from typing import Any, List, Sequence, Tuple\n",
    "\n",
    "import os\n",
    "import io\n",
    "import base64\n",
    "import time\n",
    "import glob\n",
    "from IPython.display import HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Environment - CartPole \n",
    "\n",
    "We can use the setup here to run on any environment which has state as a single vector and actions are discrete. We will build it on Cart Pole and they try to run this on many other environments like Atari games and others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_env(env_name, seed=None):\n",
    "    # remove time limit wrapper from environment\n",
    "    env = gym.make(env_name).unwrapped\n",
    "    if seed is not None:\n",
    "        env.seed(seed)\n",
    "    return env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "env_name = 'CartPole-v1'\n",
    "\n",
    "env = make_env(env_name)\n",
    "env.reset()\n",
    "plt.imshow(env.render(\"rgb_array\"))\n",
    "state_shape, n_actions = env.observation_space.shape, env.action_space.n\n",
    "state_dim = state_shape[0]\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Actor Critic Network\n",
    "\n",
    "We will build two simple networks that take in state. One network produces logits for the action probabilities. 2nd network produces the Value of the state. The observation space and action space is as given below for CartPole\n",
    "\n",
    "    Observation:\n",
    "        Type: Box(4)\n",
    "        Num     Observation               Min                     Max\n",
    "        0       Cart Position             -4.8                    4.8\n",
    "        1       Cart Velocity             -Inf                    Inf\n",
    "        2       Pole Angle                -0.418 rad (-24 deg)    0.418 rad (24 deg)\n",
    "        3       Pole Angular Velocity     -Inf                    Inf\n",
    "    Actions:\n",
    "        Type: Discrete(2)\n",
    "        Num   Action\n",
    "        0     Push cart to the left\n",
    "        1     Push cart to the right\n",
    "        \n",
    "\n",
    "Each model will be a simple one with 1 hidden layer with Relu activation and final layer being logits (for policy/actor network) and value of the state for the Critic Network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ActorCritic(tf.keras.Model):\n",
    "    \"\"\"Combined actor-critic network.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        \"\"\"Initialize.\"\"\"\n",
    "        super().__init__()\n",
    "\n",
    "        self.common = layers.Dense(192, activation=\"relu\")\n",
    "        self.actor = layers.Dense(n_actions)\n",
    "        self.critic = layers.Dense(1)\n",
    "\n",
    "    def call(self, inputs: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:\n",
    "        x = self.common(inputs)\n",
    "        return self.actor(x), self.critic(x)\n",
    "\n",
    "model = ActorCritic()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Action Probabilities\n",
    "\n",
    "We will use this function to generate the trajectory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_action(state):\n",
    "    \"\"\"\n",
    "    params: states: [batch, state_dim]\n",
    "    returns: probs: [batch, n_actions]\n",
    "    \"\"\"\n",
    "    logits,_ = model(state)\n",
    "    action_probs = tf.nn.softmax(logits, -1).numpy()[0]\n",
    "    action = np.random.choice(n_actions, p=action_probs)\n",
    "    return action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Play game and generate Trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_trajectory(env, n_steps=1000):\n",
    "    \"\"\"\n",
    "    Play a session and genrate a trajectory\n",
    "    returns: arrays of states, actions, rewards\n",
    "    \"\"\"\n",
    "    states, actions, rewards = [], [], []\n",
    "    \n",
    "    # initialize the environment\n",
    "    s = env.reset()\n",
    "    \n",
    "    #generate n_steps of trajectory:\n",
    "    for t in range(n_steps):\n",
    "        #sample action based on action_probs\n",
    "        a = sample_action(np.array([s], dtype=np.float32))\n",
    "        next_state, r, done, _ = env.step(a)\n",
    "        \n",
    "        #update arrays\n",
    "        states.append(s)\n",
    "        actions.append(a)\n",
    "        rewards.append(r)\n",
    "\n",
    "        s = next_state\n",
    "        if done:\n",
    "            break\n",
    "    \n",
    "    return states, actions, rewards"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Rewards to Go\n",
    "\n",
    " $G(s_t) = \\sum_{t'=t}^{T} \\gamma^{t-t'} r(s_{t'}^i, a_{t'}^i)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_rewards_to_go(rewards, gamma=0.99):\n",
    "    \n",
    "    T = len(rewards) # total number of individual rewards\n",
    "    # empty array to return the rewards to go\n",
    "    rewards_to_go = [0]*T \n",
    "    rewards_to_go[T-1] = rewards[T-1]\n",
    "    \n",
    "    for i in range(T-2, -1, -1): #go from T-2 to 0\n",
    "        rewards_to_go[i] = gamma * rewards_to_go[i+1] + rewards[i]\n",
    "    \n",
    "    return rewards_to_go"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train on one trajectory\n",
    "\n",
    "We will calculate the loss and take a gradient step. We will use Adam Optimizer\n",
    "\n",
    "**policy network loss:**\n",
    "\n",
    "We are taking only one trajectory. so N=1. We will, however, average it over the number of actions to get the average loss. So the function we will actually implement is as given below:\n",
    "\n",
    "\n",
    "$$Loss(\\theta, \\phi) = - J(\\theta, \\phi) - H(\\pi_\\theta(a_t|s_t)) = - \\frac{1}{T}  \\left[ \\sum_{t=1}^{T} \\left(  \\log{ \\pi_\\theta(a_t|s_t)} \\left[ \\hat{Q}(s_t,\\ a_t) - V_\\phi(s_t)\\right] \\right) - \\beta \\sum_{a} \\pi_\\theta(a|s_t).\\log{ \\pi_\\theta(a|s_t)} \\right] $$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#init Optimizer\n",
    "optimizer = tf.keras.optimizers.Adam()\n",
    "\n",
    "def train_one_episode(states, actions, rewards, gamma=0.99, entropy_coef=1e-2):\n",
    "    \n",
    "    # get rewards to go\n",
    "    rewards_to_go = get_rewards_to_go(rewards, gamma)\n",
    "\n",
    "    # convert to numpy arrays\n",
    "    states = np.array(states, dtype=np.float32)\n",
    "    actions = np.array(actions, dtype=np.int)\n",
    "    rewards_to_go = np.array(rewards_to_go, dtype=np.float32)\n",
    "    \n",
    "    with tf.GradientTape() as tape:\n",
    "\n",
    "        # get action probabilities from states\n",
    "        logits, state_values = model(states)\n",
    "        probs = tf.nn.softmax(logits, -1)\n",
    "        log_probs = tf.nn.log_softmax(logits, -1)\n",
    "    \n",
    "        row_indices= tf.range(len(actions))\n",
    "        indices = tf.transpose([row_indices, actions])\n",
    "        log_probs_for_actions = tf.gather_nd(log_probs, indices)\n",
    "    \n",
    "        advantage = rewards_to_go - state_values\n",
    "\n",
    "        #Compute loss to be minized\n",
    "        J = tf.reduce_mean(log_probs_for_actions*advantage)\n",
    "        H = -tf.reduce_mean(tf.reduce_sum(probs*log_probs, -1))\n",
    "    \n",
    "        loss = -(J+entropy_coef*H)\n",
    "\n",
    "    grads = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "    \n",
    "    return np.sum(rewards) #to show progress on training\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean reward:23.737\n",
      "mean reward:43.111\n",
      "mean reward:151.020\n",
      "mean reward:233.626\n",
      "mean reward:350.455\n",
      "mean reward:536.020\n",
      "mean reward:917.515\n"
     ]
    }
   ],
   "source": [
    "total_rewards = []\n",
    "for i in range(10000):\n",
    "    states, actions, rewards = generate_trajectory(env)\n",
    "    reward = train_one_episode(states, actions, rewards)\n",
    "    total_rewards.append(reward)\n",
    "    if i != 0 and i % 100 == 0:\n",
    "        mean_reward = np.mean(total_rewards[-100:-1])\n",
    "        print(\"mean reward:%.3f\" % (mean_reward))\n",
    "        if mean_reward > 700:\n",
    "            break\n",
    "env.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let us record a video of trained agent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_animation(env, save_dir):\n",
    "    try:\n",
    "        env = gym.wrappers.Monitor(\n",
    "            env, save_dir, video_callable=lambda id: True, force=True, mode='evaluation')\n",
    "    except gym.error.Error as e:\n",
    "        print(e)\n",
    "\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "        \n",
    "    generate_trajectory(env)\n",
    "    \n",
    "def display_animation(filepath):\n",
    "    video = io.open(filepath, 'r+b').read()\n",
    "    encoded = base64.b64encode(video)\n",
    "    return HTML(data='''<video alt=\"test\" controls>\n",
    "                <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n",
    "                 </video>'''.format(encoded.decode('ascii')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video alt=\"test\" controls>\n",
       "                <source src=\"data:video/mp4;base64,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\" type=\"video/mp4\" />\n",
       "                 </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Animate learned policy\n",
    "save_dir='./videos/tensorflow/actor_critic/'\n",
    "env = make_env(env_name)\n",
    "generate_animation(env, save_dir=save_dir)\n",
    "[filepath] = glob.glob(os.path.join(save_dir, '*.mp4'))\n",
    "display_animation(filepath)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
